Abstract
Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive models. These models use aggregated and anonymized card usage patterns, including cross-border transactions and industry-specific spending, to tailor bank offerings and maximize revenue opportunities. Mastercard has established an AI Governance program, based on its Data and Tech Responsibility Principles, to evaluate any built and bought AI for efficacy, fairness, and transparency. As part of this effort, Mastercard has sought expertise from the Turing Institute through a Data Study Group to better assess fairness in more complex AI/ML models. The Data Study Group challenge lies in defining, measuring, and mitigating fairness in these predictions, which can be complex due to the various interpretations of fairness, gaps in the research literature, and ML-operations challenges.
Citation information
Data Study Group Team. (2024). Data Study Group Final Report: Mastercard - Measuring Fairness in Financial Transaction Machine Learning Models (Version 1). The Alan Turing Institute. https://doi.org/10.5281/zenodo.14528864
Additional information
Deniz Sezin Ayvaz
Lorenzo Belenguer
Hankun He, Queen Mary University of London
Deborah Dormah Kanubala, Saarland University
Mingxu Li, Southwest Jiaotong University
Soung Low
Faithful Chiagoziem Onwuegbuche, University College Dublin
Yulu Pi, University of Warwick
Dan Tran, Catolica Lisbon School of Business
Shresth Verma, Harvard /university
Hanzhi Wang, Cardiff University
Skyler Xie, University of Warwick
Carlos Mougan, The Alan Turing Institute (PI)
Adeline Pelletier, Mastercard (Challenge Owner)